Targets’ report corresponding to the functions that execute de Mixomics part of the IODA pipeline…
Data loaded from file data/new.raw.data.Rda (Features in rows; Samples in columns)
## A0FJ A13E A0G0 A0SX A143 A0DA A0B3 A0I2
## RTN2 4.362183 1.984492 1.727323 4.363996 2.447562 4.770798 3.3520618 1.810382
## NDRG2 7.533461 7.455194 8.079968 5.793750 7.158993 8.748061 5.0984040 3.791965
## CCDC113 3.956124 5.427623 2.227300 3.544866 4.691256 4.305401 0.5932056 2.719169
## FAM63A 4.457170 5.440957 5.543480 4.737114 4.808728 5.307480 5.2175851 4.355919
## ACADS 2.256817 4.028813 2.629855 4.269101 2.442135 3.239909 3.8851534 4.200249
## GMDS 6.017940 4.341692 6.363030 4.001104 7.029723 4.236539 5.9178858 4.830286
## HLA-H 5.006907 6.178668 6.039563 7.087633 5.936138 6.909727 8.0433411 9.130370
## SEMA4A 3.217812 2.864659 5.946028 5.007565 5.901459 6.591109 6.5328925 4.982386
## ETS2 4.734446 5.411029 5.651670 5.902449 6.641225 5.858016 6.3091167 5.304488
## LIMD2 5.099598 4.211397 3.304513 5.479451 5.508654 3.766283 4.1138727 5.149344
## A0FJ A13E A0G0 A0SX A143 A0DA A0B3 A0I2
## YWHAE 0.04913078 -0.07998211 -0.03284989 -0.20532949 0.06019021 0.03076171 -0.107861537 0.64984396
## EIF4EBP1 0.44748623 0.60521842 0.89460973 -0.14132292 0.13176899 0.03299680 -0.037124691 -0.52148657
## TP53BP1 0.91783419 0.05910121 0.51704453 -0.31372867 0.33091238 -0.22027100 -0.544743061 -1.60203535
## ARAF 0.02274147 -0.45985298 -0.19182192 -0.07482347 -0.02435747 0.41861665 0.430503500 -0.18714658
## ACACA -0.08626782 -0.59269183 0.41117190 -0.85148060 0.76975143 -0.71430870 -0.363474049 1.07761482
## ACCB -0.41662442 -0.06226840 0.82582859 -0.66341044 0.87347870 -0.21752677 -0.269313837 1.58998239
## PRKAA1 0.28527039 -0.27523360 0.06774184 0.02956373 -0.21653182 -0.06306506 -0.077581092 -0.07753959
## ANLN 0.17231110 0.22210598 0.12199399 1.05494810 0.01378422 0.06025690 0.008872461 -0.05187936
## AR -1.30760569 -1.62047596 -1.07789444 -1.26705469 -0.60132744 -1.20803848 -1.016297633 -0.42122691
## ARID1A 0.50509449 0.33958160 0.22718066 0.35529767 0.54412514 -0.11094480 -0.233223615 -0.35537533
p <- heatmaply(gene_data,
#dendrogram = "row",
xlab = "", ylab = "",
main = "",
scale = "column",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.0000001,
titleX = FALSE,
hide_colorbar = TRUE,
branches_lwd = 0.1,
label_names = c("Gene", "Sample", "Value"),
fontsize_row = 5, fontsize_col = 5,
labCol = colnames(gene_data),
labRow = rownames(gene_data),
heatmap_layers = theme(axis.line=element_blank())
)
p
# save the widget
# library(htmlwidgets)
# saveWidget(p, file= "~/.../heatmap.html")
(Features in rows; Samples in columns)
## A0FJ A13E A0G0 A0SX A143 A0DA A0B3 A0I2
## RTN2 -0.339017216 -1.7069589 -1.6932017 -0.77077034 -1.5125563 -0.30827331 -1.10524419 -1.78686096
## NDRG2 1.351995219 1.2645533 1.5640835 0.17573315 1.1455179 1.88852090 -0.09520148 -0.59147057
## CCDC113 -0.555539075 0.1632410 -1.4368410 -1.31303795 -0.2467189 -0.56532934 -2.70090082 -1.23863485
## FAM63A -0.288367464 0.1704837 0.2635129 -0.52376439 -0.1804445 -0.01184293 -0.02626998 -0.25126554
## ACADS -1.461656232 -0.5965483 -1.2304330 -0.83359119 -1.5156183 -1.15384143 -0.79691673 -0.34517364
## GMDS 0.543877815 -0.4266024 0.6837324 -1.01100612 1.0725867 -0.60336459 0.37876721 0.03489636
## HLA-H 0.004767261 0.5711851 0.5178763 1.03228877 0.4556130 0.87313916 1.60807992 2.62892171
## SEMA4A -0.949227217 -1.2288804 0.4699170 -0.34472468 0.4360478 0.69715404 0.73447246 0.12665046
## ETS2 -0.140516320 0.1542277 0.3189866 0.24769245 0.8534059 0.29223902 0.60504583 0.32095851
## LIMD2 0.054192600 -0.4973748 -0.8845059 -0.03233419 0.2144367 -0.86310523 -0.66463095 0.22736789
## A0FJ A13E A0G0 A0SX A143 A0DA A0B3 A0I2
## YWHAE 0.048086807 -0.06835724 -0.03680699 -0.37011394 0.15445759 0.17963331 -0.15661380 0.82210223
## EIF4EBP1 0.718910137 0.91548414 1.15538628 -0.26601103 0.24245583 0.18352769 -0.01907974 -0.45387748
## TP53BP1 1.510967539 0.13134476 0.67004904 -0.54641881 0.48728075 -0.25776165 -1.00604509 -1.63096482
## ARAF 0.003647689 -0.61379274 -0.24115592 -0.15785359 0.05051551 0.85542489 0.89013248 -0.08966685
## ACACA -0.179921971 -0.80452867 0.53395617 -1.42104063 1.02678515 -1.11856420 -0.65360269 1.28809104
## ACCB -0.736236460 -0.04292312 1.06697229 -1.11515560 1.15430643 -0.25298015 -0.47052645 1.84623463
## PRKAA1 0.445741613 -0.34870802 0.09249759 0.01192604 -0.18574175 0.01615120 -0.09773935 0.02973266
## ANLN 0.255520229 0.36539426 0.16223545 1.67965330 0.09740652 0.23102520 0.07035279 0.05768544
## AR -2.236632707 -2.28026681 -1.38014859 -2.09694741 -0.65880568 -1.97883024 -1.92289139 -0.34466042
## ARID1A 0.815921388 0.53407098 0.29744656 0.54171311 0.74940241 -0.06727361 -0.40035602 -0.27292552
p <- heatmaply(gene_sc,
#dendrogram = "row",
xlab = "", ylab = "",
main = "",
scale = "column",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.0000001,
titleX = FALSE,
hide_colorbar = TRUE,
branches_lwd = 0.1,
label_names = c("Gene", "Sample", "Value"),
fontsize_row = 5, fontsize_col = 5,
labCol = colnames(gene_sc),
labRow = rownames(gene_sc),
heatmap_layers = theme(axis.line=element_blank())
)
p
# save the widget
# library(htmlwidgets)
# saveWidget(p, file= "~/.../heatmap.html")
plot_corr_matrix(tar_read(X), tar_read(Y), p.resultsDir)
## png
## 2
Correlation matrix
heatmaply_cor(
cor(X),
xlab = "Genes",
ylab = "Genes"#,
#k_col = 2,
#k_row = 2
)
heatmaply_cor(
cor(Y),
xlab = "Prots",
ylab = "Prots"#,
#k_col = 2,
#k_row = 2
)
tar_read(rCCA)
##
## Call:
## rcc(X = X, Y = Y, ncomp = 3, lambda1 = lambda1, lambda2 = lambda2)
##
## rCCA with 3 components and regularization parameters 0.1 and 0.02 for the X and Y data.
## You entered data X of dimensions : 150 200
## You entered data Y of dimensions : 150 110
##
## Main numerical outputs:
## --------------------
## canonical correlations: see object$cor
## loading vectors: see object$loadings
## variates: see object$variates
## variable names: see object$names
plot_indiv(tar_read(rCCA), p.resultsDir)
## png
## 2
Correlation circles plots (at cutoff points: 0.5, 0.6, 0.7)
for(i in 1:length(p.circ.cutOffs)){
plot_corrCirc(tar_read(rCCA), p.resultsDir, cutOff = p.circ.cutOffs[i])
}
str(tar_read(rCCA_tagged))
## List of 11
## $ call : language rcc(X = X, Y = Y, ncomp = 3, lambda1 = lambda1, lambda2 = lambda2)
## $ X : num [1:150, 1:200] -0.339 -1.707 -1.693 -0.771 -1.513 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## .. ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
## ..- attr(*, "scaled:center")= Named num [1:150] 5 5.13 5.03 5.53 5.13 ...
## .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## ..- attr(*, "scaled:scale")= Named num [1:150] 1.88 1.84 1.95 1.51 1.77 ...
## .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## $ Y : num [1:150, 1:110] 0.0481 -0.0684 -0.0368 -0.3701 0.1545 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## .. ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
## ..- attr(*, "scaled:center")= Named num [1:150] 0.02058 -0.03237 -0.00422 0.02223 -0.06545 ...
## .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## ..- attr(*, "scaled:scale")= Named num [1:150] 0.594 0.696 0.778 0.615 0.813 ...
## .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## $ ncomp : num 3
## $ method : chr "ridge"
## $ cor : Named num [1:110] 0.985 0.951 0.944 0.937 0.928 ...
## ..- attr(*, "names")= chr [1:110] "1" "2" "3" "4" ...
## $ loadings :List of 2
## ..$ X: num [1:200, 1:3] -0.00629 0.05236 -0.03147 -0.02301 0.00386 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:200] "RTN2" "NDRG2" "CCDC113" "FAM63A" ...
## .. .. ..$ : NULL
## ..$ Y: num [1:110, 1:3] 0.0237 -0.0155 -0.0122 -0.0812 -0.0339 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:110] "YWHAE" "EIF4EBP1" "TP53BP1" "ARAF" ...
## .. .. ..$ : NULL
## $ variates :List of 2
## ..$ X: num [1:150, 1:3] 1.414 1.612 1.207 0.982 1.215 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## .. .. ..$ : NULL
## ..$ Y: num [1:150, 1:3] 1.41 1.6 1.22 1.07 1.21 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## .. .. ..$ : NULL
## $ names :List of 4
## ..$ sample : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
## ..$ colnames:List of 2
## .. ..$ X: chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
## .. ..$ Y: chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
## ..$ blocks : chr [1:2] "X" "Y"
## ..$ data : chr [1:2] "X" "Y"
## $ lambda : Named num [1:2] 0.1 0.02
## ..- attr(*, "names")= chr [1:2] "lambda1" "lambda2"
## $ prop_expl_var:List of 2
## ..$ X: Named num [1:3] 0.0742 0.0174 0.0184
## .. ..- attr(*, "names")= chr [1:3] "comp1" "comp2" "comp3"
## ..$ Y: Named num [1:3] 0.232 0.0444 0.0213
## .. ..- attr(*, "names")= chr [1:3] "comp1" "comp2" "comp3"
## - attr(*, "class")= chr "rcc"
str(tar_read(rCCA_network))
## List of 3
## $ gR :List of 10
## ..$ :List of 1
## .. ..$ x.FAM63A: 'igraph.vs' Named int 42
## .. .. ..- attr(*, "names")= chr "y.ESR1"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.LIMD2: 'igraph.vs' Named int 42
## .. .. ..- attr(*, "names")= chr "y.ESR1"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.RTKN2: 'igraph.vs' Named int [1:2] 42 43
## .. .. ..- attr(*, "names")= chr [1:2] "y.ESR1" "y.GATA3"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.ARL4C: 'igraph.vs' Named int 42
## .. .. ..- attr(*, "names")= chr "y.ESR1"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.ASPM: 'igraph.vs' Named int [1:7] 38 39 40 41 42 43 45
## .. .. ..- attr(*, "names")= chr [1:7] "y.ASNS" "y.CDK1" "y.CCNB1" "y.CCNE1" ...
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.KDM4B: 'igraph.vs' Named int [1:8] 38 39 40 41 42 43 44 45
## .. .. ..- attr(*, "names")= chr [1:8] "y.ASNS" "y.CDK1" "y.CCNB1" "y.CCNE1" ...
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.SNORA8: 'igraph.vs' Named int 43
## .. .. ..- attr(*, "names")= chr "y.GATA3"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.ZNF552: 'igraph.vs' Named int [1:9] 37 38 39 40 41 42 43 44 45
## .. .. ..- attr(*, "names")= chr [1:9] "y.AR" "y.ASNS" "y.CDK1" "y.CCNB1" ...
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.ASF1B: 'igraph.vs' Named int 42
## .. .. ..- attr(*, "names")= chr "y.ESR1"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..$ :List of 1
## .. ..$ x.FUT8: 'igraph.vs' Named int [1:4] 37 41 42 43
## .. .. ..- attr(*, "names")= chr [1:4] "y.AR" "y.CCNE1" "y.ESR1" "y.GATA3"
## .. .. ..- attr(*, "env")=<weakref>
## .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
## ..- attr(*, "class")= chr "igraph"
## $ M : num [1:200, 1:110] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
## .. ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
## $ cutoff: num 0.5
str(rCCA_network$M)
## num [1:200, 1:110] 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
## ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
Correlation network
mat <- data.matrix(rCCA_network$M)
colnames(mat) <- colnames(rCCA_network$M)
rownames(mat) <- rownames(rCCA_network$M)
str(mat)
## num [1:200, 1:110] 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
## ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
which(rowSums(abs(mat))!=0)
## x.FAM63A x.LIMD2 x.RTKN2 x.ARL4C x.ASPM x.KDM4B x.SNORA8 x.ZNF552 x.ASF1B x.FUT8 x.EPHB3
## 4 10 15 19 24 25 29 34 41 42 49
## x.PRNP x.C4orf34 x.LRIG1 x.STC2 x.LAPTM4B x.CSRP2 x.LYN x.SLC43A3 x.HN1 x.TTC39A x.RIN3
## 51 61 62 73 82 94 96 101 102 103 107
## x.C1orf38 x.NTN4 x.FMNL2 x.E2F1 x.CCNA2 x.NCAPG2 x.LMO4 x.NCOA7 x.RUNX3 x.SLC5A6 x.PREX1
## 109 112 116 128 136 141 153 158 162 163 165
## x.PLAUR x.MEX3A x.SEMA3C
## 166 173 195
which(colSums(abs(mat))!=0)
## y.AR y.ASNS y.CDK1 y.CCNB1 y.CCNE1 y.ESR1 y.GATA3 y.INPP4B y.PGR
## 9 11 26 33 35 40 45 51 71
mat <- mat[which(rowSums(abs(mat))!=0), which(colSums(abs(mat))!=0)]
head(mat)
## y.AR y.ASNS y.CDK1 y.CCNB1 y.CCNE1 y.ESR1 y.GATA3 y.INPP4B y.PGR
## x.FAM63A 0 0.0000000 0.0000000 0.0000000 0.0000000 0.5089002 0.0000000 0.0000000 0.0000000
## x.LIMD2 0 0.0000000 0.0000000 0.0000000 0.0000000 -0.5441847 0.0000000 0.0000000 0.0000000
## x.RTKN2 0 0.0000000 0.0000000 0.0000000 0.0000000 -0.5478270 -0.5000234 0.0000000 0.0000000
## x.ARL4C 0 0.0000000 0.0000000 0.0000000 0.0000000 -0.5029863 0.0000000 0.0000000 0.0000000
## x.ASPM 0 0.5453709 0.5584230 0.5785073 0.5574985 -0.6557532 -0.6001769 0.0000000 -0.5451481
## x.KDM4B 0 -0.5945328 -0.5641563 -0.5905025 -0.5424131 0.6871384 0.6091690 0.5531793 0.6028017
p <- heatmaply_cor(
mat,
xlab = "Prots",
ylab = "Genes"#,
#k_col = 2,
#k_row = 2
)
p
# save the widget
# library(htmlwidgets)
# saveWidget(p, file= "~/.../heatmap.html")
plot_cim(tar_read(rCCA_tagged), p.resultsDir, x.lab = p.x.lab, y.lab = p.y.lab)
## png
## 2
CIM Heatmap